190 citations · 200 across the 5 of their papers we have counts for
5 papers
Instance-Level Data-Use Auditing of Visual ML Models
Zonghao Huang, Neil Zhenqiang Gong, Michael K. Reiter
The growing trend of legal disputes over the unauthorized use of data in machine learning (ML) systems highlights the urgent need for reliable data-use auditing mechanisms to ensur…
A General Framework for Data-Use Auditing of ML Models
Zonghao Huang, Neil Zhenqiang Gong, Michael K. Reiter
Auditing the use of data in training machine-learning (ML) models is an increasingly pressing challenge, as myriad ML practitioners routinely leverage the effort of content creator…
Mendata: A Framework to Purify Manipulated Training Data
Zonghao Huang, Neil Gong, Michael K. Reiter
Untrusted data used to train a model might have been manipulated to endow the learned model with hidden properties that the data contributor might later exploit. Data purification…
The Impact of Exposed Passwords on Honeyword Efficacy
Zonghao Huang, Lujo Bauer, Michael K. Reiter
Honeywords are decoy passwords that can be added to a credential database; if a login attempt uses a honeyword, this indicates that the site's credential database has been leaked.…
DP-ADMM: ADMM-based Distributed Learning with Differential Privacy
Zonghao Huang, Rui Hu, Yuanxiong Guo +2
Alternating Direction Method of Multipliers (ADMM) is a widely used tool for machine learning in distributed settings, where a machine learning model is trained over distributed da…